Converged RAN/MEC slicing in beyond 5G (B5G) networks
(English) The main objective of this thesis is to propose solutions for implementing dynamic RAN slicing and Functional Split (FS) along with MEC placements in 5G/B5G. In particular, this thesis is divided into three parts. In the first part (Chapter 3), we model a joint slicing and FS optimization...
| Autor: | |
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/690188 |
| Acceso en línea: | http://hdl.handle.net/10803/690188 https://dx.doi.org/10.5821/dissertation-2117-403562 |
| Access Level: | acceso abierto |
| Palabra clave: | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació 621.3 |
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Converged RAN/MEC slicing in beyond 5G (B5G) networks |
| title |
Converged RAN/MEC slicing in beyond 5G (B5G) networks |
| spellingShingle |
Converged RAN/MEC slicing in beyond 5G (B5G) networks Ojaghi Kahjogh, Behnam Àrees temàtiques de la UPC::Enginyeria de la telecomunicació 621.3 |
| title_short |
Converged RAN/MEC slicing in beyond 5G (B5G) networks |
| title_full |
Converged RAN/MEC slicing in beyond 5G (B5G) networks |
| title_fullStr |
Converged RAN/MEC slicing in beyond 5G (B5G) networks |
| title_full_unstemmed |
Converged RAN/MEC slicing in beyond 5G (B5G) networks |
| title_sort |
Converged RAN/MEC slicing in beyond 5G (B5G) networks |
| dc.creator.none.fl_str_mv |
Ojaghi Kahjogh, Behnam |
| author |
Ojaghi Kahjogh, Behnam |
| author_facet |
Ojaghi Kahjogh, Behnam |
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author |
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Adelantado Freixer, Ferran Verikoukis, Christos Alonso Zárate, Luis Gonzaga Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
| dc.subject.none.fl_str_mv |
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació 621.3 |
| topic |
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació 621.3 |
| description |
(English) The main objective of this thesis is to propose solutions for implementing dynamic RAN slicing and Functional Split (FS) along with MEC placements in 5G/B5G. In particular, this thesis is divided into three parts. In the first part (Chapter 3), we model a joint slicing and FS optimization in the 5G RAN with the objectives of optimizing the centralization degree and throughput. In this work, the RAN slicing allowed a customized FS deployment per slice, thus optimizing the available resources, e.g., transport network capacity and Remote Radio Head (RRH) or Central Unit (CU) computational capacity. Next, we present the second part in Chapter 4 by extending the first work by proposing SlicedRAN: service-aware network slicing framework for 5G RAN to create isolated RAN slices based on the service requirements with customized functional splits per slice. The proposed framework investigates the bottlenecks in the capacity of RRHs Fronthaul/Backhaul (FH/BH) network capacity along with a minimum level of Service Level Agreement (SLA) for each slice imposed by the different service types. Finally, in the last part presented in Chapter 5, we investigate dynamic RAN/MEC slicing framework in Open-RAN (O-RAN) architecture to dynamically place the RAN protocol stack of Virtual Network Functions (VNFs) and MEC server per slice. This framework contains the bottlenecks in the capacity of Open-RAN Radio Units (O-RUs), MEC server computation capacity, together with a customized FS per slice, to jointly solve the challenge of operating cost-efficient edge networks and maintaining the served traffic with various QoS criteria. We use a robust Benders decomposition algorithm, which reduces the computation complexity while ensuring an exact and optimal global solution. The proposed algorithm successfully optimizes the joint throughput and system cost in various traffic scenarios while satisfying QoS criteria, as shown by trace-driven simulation results. Hence, in order to determine the right MEC settings for on-demand traffic and alter the MEC type to satisfy the QoS requirements of various User Equipment (UEs) belonging to different slice types, we explore the compute and storage capacity for MEC services such as Enhanced Mobile Broadband (eMBB) and ultra-Reliable and Low-Latency Communications (uRLLC). The overall conclusion of the present findings demonstrates a trade-off between the throughput attained and the cost incurred to the network. As a result, we investigate multi-objective optimization to construct slices while optimizing throughput and decreasing computational cost objectives, and we compare its performance to that of a single objective (maximizing throughput). The findings demonstrate that a throughput increase of up to 160% can be made possible by increasing 78% in the computation cost for a single objective when compared with multi-objective without prioritization. In addition, comparing a single objective with a multi-objective with priority in throughput, it increases throughput by up to 82% and adds 17% to computation costs. Consequently, a single objective of maximizing throughput can result in high throughput at the expense of high cost. It is possible to achieve almost half the amount of throughput using multi-objective with prioritization in throughput, whereas costs can be reduced five-fold. |
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2023 |
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2023 2024 2024 |
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info:eu-repo/semantics/doctoralThesis info:eu-repo/semantics/publishedVersion |
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http://hdl.handle.net/10803/690188 https://dx.doi.org/10.5821/dissertation-2117-403562 |
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http://hdl.handle.net/10803/690188 https://dx.doi.org/10.5821/dissertation-2117-403562 |
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Inglés |
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Inglés |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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openAccess |
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114 p. application/pdf |
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Universitat Politècnica de Catalunya |
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Universitat Politècnica de Catalunya |
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TDX (Tesis Doctorals en Xarxa) reponame:TDR. Tesis Doctorales en Red instname:CBUC, CESCA |
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TDR. Tesis Doctorales en Red |
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1869421675479564288 |
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Converged RAN/MEC slicing in beyond 5G (B5G) networksOjaghi Kahjogh, BehnamÀrees temàtiques de la UPC::Enginyeria de la telecomunicació621.3(English) The main objective of this thesis is to propose solutions for implementing dynamic RAN slicing and Functional Split (FS) along with MEC placements in 5G/B5G. In particular, this thesis is divided into three parts. In the first part (Chapter 3), we model a joint slicing and FS optimization in the 5G RAN with the objectives of optimizing the centralization degree and throughput. In this work, the RAN slicing allowed a customized FS deployment per slice, thus optimizing the available resources, e.g., transport network capacity and Remote Radio Head (RRH) or Central Unit (CU) computational capacity. Next, we present the second part in Chapter 4 by extending the first work by proposing SlicedRAN: service-aware network slicing framework for 5G RAN to create isolated RAN slices based on the service requirements with customized functional splits per slice. The proposed framework investigates the bottlenecks in the capacity of RRHs Fronthaul/Backhaul (FH/BH) network capacity along with a minimum level of Service Level Agreement (SLA) for each slice imposed by the different service types. Finally, in the last part presented in Chapter 5, we investigate dynamic RAN/MEC slicing framework in Open-RAN (O-RAN) architecture to dynamically place the RAN protocol stack of Virtual Network Functions (VNFs) and MEC server per slice. This framework contains the bottlenecks in the capacity of Open-RAN Radio Units (O-RUs), MEC server computation capacity, together with a customized FS per slice, to jointly solve the challenge of operating cost-efficient edge networks and maintaining the served traffic with various QoS criteria. We use a robust Benders decomposition algorithm, which reduces the computation complexity while ensuring an exact and optimal global solution. The proposed algorithm successfully optimizes the joint throughput and system cost in various traffic scenarios while satisfying QoS criteria, as shown by trace-driven simulation results. Hence, in order to determine the right MEC settings for on-demand traffic and alter the MEC type to satisfy the QoS requirements of various User Equipment (UEs) belonging to different slice types, we explore the compute and storage capacity for MEC services such as Enhanced Mobile Broadband (eMBB) and ultra-Reliable and Low-Latency Communications (uRLLC). The overall conclusion of the present findings demonstrates a trade-off between the throughput attained and the cost incurred to the network. As a result, we investigate multi-objective optimization to construct slices while optimizing throughput and decreasing computational cost objectives, and we compare its performance to that of a single objective (maximizing throughput). The findings demonstrate that a throughput increase of up to 160% can be made possible by increasing 78% in the computation cost for a single objective when compared with multi-objective without prioritization. In addition, comparing a single objective with a multi-objective with priority in throughput, it increases throughput by up to 82% and adds 17% to computation costs. Consequently, a single objective of maximizing throughput can result in high throughput at the expense of high cost. It is possible to achieve almost half the amount of throughput using multi-objective with prioritization in throughput, whereas costs can be reduced five-fold.(Español) El principal objetivo de esta tesis es proponer soluciones para implementar RAN slicing dinámico y División funcional (FS) junto con ubicaciones MEC en 5G/B5G. En particular, esta tesis se divide en tres partes. En la primera parte (Capítulo 3), modelamos la optimización conjunta de slicing y FS en 5G RAN con los objetivos de optimizar el grado de centralización y el rendimiento. En este trabajo, RAN slicing permitió una implementación personalizada de FS por slice, optimizando así los recursos disponibles, por ejemplo, la capacidad de la red de transporte y de los Remote Radio Heads (RRH) o de la Unidad Central (CU). A continuación, presentamos la segunda parte en el Capítulo 4, que es una extensión del primer trabajo y propone SlicedRAN, un marco para el slicing de redes de acceso 5G basado en los servicios, que crea FSs personalizados para cada slice. El marco propuesto investiga la cuellos de botella en la capacidad de los RRH, la capacidad de la red Fronthaul/Backhaul (FH/BH) junto con una nivel mínimo de Service Level Agreement (SLA) para cada slice. Finalmente, en la última parte presentada en el Capítulo 5, investigamos el marco para el RAN/MEC slicing dinámico en la arquitectura Open-RAN (O-RAN) y así colocar dinámicamente la pila de protocolos RAN de Funciones de Red Virtuales (VNF) y el servidor MEC por slice. Este marco contiene los cuellos de botella en la capacidad de las unidades de radio Open-RAN (O-RU), la capacidad de cómputo del servidor MEC, y el FS personalizado por slice, y permite resolver conjuntamente un doble desafío: operar la red de forma eficiente en términos de coste; y mantener el tráfico servido con distintos criterios de calidad de servicio. Utilizamos un algoritmo robusto de descomposición de Benders, que reduce la complejidad de cálculo al tiempo que garantiza una solución global exacta y óptima. El algoritmo propuesto optimiza con éxito el rendimiento conjunto y el coste del sistema para ´ varios tipos de escenarios de tráfico mientras se satisfacen los criterios de QoS, tal y como se muestra en los resultados de la simulación. Por lo tanto, para determinar la configuración correcta de MEC para el tráfico bajo demanda y modificar el tipo de MEC para satisfacer los requisitos de QoS de varios Equipos de Usuario (UE) pertenecientes a diferentes slices, exploramos la capacidad de cómputo y almacenamiento para varios servicios MEC. La conclusión general de los hallazgos demuestra compromiso entre el rendimiento obtenido y el coste incurrido por la red. Como resultado, investigamos la optimización multiobjetivo para construir slices mientras optimizamos el rendimiento y disminuimos los objetivos de coste, y comparamos su rendimiento con el de un solo objetivo (maximizar el rendimiento). Los hallazgos demuestran que, cuando las prioridades para los dos objetivos son iguales, se puede lograr un aumento del rendimiento de hasta un 160 % aumentando el coste de computación en un 78%. Además, cuando se prioriza el rendimiento, el aumento en el rendimiento es de aproximadamente un 82%, mientras que el coste de cómputo aumenta un 17%.DOCTORAT EN TEORIA DEL SENYAL I COMUNICACIONS (Pla 2013)Universitat Politècnica de CatalunyaAdelantado Freixer, FerranVerikoukis, ChristosAlonso Zárate, Luis GonzagaUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions202420242023info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/publishedVersion114 p.application/pdfhttp://hdl.handle.net/10803/690188https://dx.doi.org/10.5821/dissertation-2117-403562TDX (Tesis Doctorals en Xarxa)reponame:TDR. Tesis Doctorales en Redinstname:CBUC, CESCAInglésL'accés als continguts d'aquesta tesi queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: http://creativecommons.org/licenses/by-nc-sa/4.0/http://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:www.tdx.cat:10803/6901882026-06-14T12:46:07Z |
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15,300724 |